Recently, tremendous success has been achieved in constructing a catalog of genetic variants in disease genomes or across population. The next great challenge is to elucidate the potential function of various genetic variants in biological an disease processes. An important type of functional variants consists of those that affect gene expression in cis. Indeed, cis-regulatory variants are involved in a broad range of diseases and they showed a consistently stronger influence on gene expression than trans-acting determinants. Alternative splicing is an essential mechanism via which cis-regulatory changes may occur. Previous studies estimated that 15- 60% of point mutations that result in human genetic diseases disrupt splicing, highlighting the importance of this regulatory step. In addition to the well-known splice site signals, splicing is closely regulated by many exonic or intronic cis elements, associated with trans-acting proteins. Disruption of these cis-regulatory elements can cause aberrant splicing. Yet this crucial regulatory aspect remains largely unexplored. We propose to combine computational, genomic and molecular approaches to study splicing changes due to genetic variations. The specific aims are: (1) To globally identify exons and genes that are under differential splicing regulation by the alternative alleles of genetic variant, via bioinformatic analysis of high-throughput sequencing of transcriptome profiles (RNA-Seq). (2) To identify causal genetic variants in splicing alteration using minigene-based experiments. (3) To develop an integrative model to predict causal genetic variants in splicing alteration, using machine learning approaches, RNA- Seq data and molecular validations. This project will elucidate functional cis-regulatory genetic variants in splicing and provide significant insight ino the involvement of genetic variations in human diseases. In addition, this work will generate valuable bioinformatic tools to make full use of the increasingly available RNA-Seq data in a wide variety of cell types for identification and prediction of disease-related genetic variants.